Abstract
Electricity Market uses Demand and Supply chain strategy. Also, it is prone to random fluctuations that directly impact profit. Therefore forecasting demand becomes very important to mitigate the consequences of price dynamics. This paper proposes a Deep Learning model using Long Short Term Memory (LSTM) and Convolution Neural Network to forecast future electricity prices on the Australian electricity market and compares them with other state of the art models. We have selected evaluation metrics to prove that our model outperforms the other existing models for electricity price prediction. Copyright © 2020 by the Institute of Electrical and Electronics Engineers, Inc. All Rights Reserved.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020 |
| Place of Publication | Danvers, MA |
| Publisher | IEEE |
| ISBN (Electronic) | 9781728186054 |
| DOIs | |
| Publication status | Published - Nov 2020 |
Citation
MITTAL, D. A., Liu, S., & Xu, G. (2020). Electricity price forecasting using convolution and LSTM models. In Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020. IEEE. https://doi.org/10.1109/BESC51023.2020.9348313Keywords
- Electricity Price Forecasting
- LSTM
- Convolution
- Neural Networks